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This article offers a systematic analysis of 727 manuscripts that used Reddit as a data source, published between 2010 and 2020. Our analysis reveals the increasing growth in use of Reddit as a data source, the range of disciplines this research is occurring in, how researchers are getting access to Reddit data, the characteristics of the datasets researchers are using, the subreddits and topics being studied, the kinds of analysis and methods researchers are engaging in, and the emerging ethical questions of research in this space. We discuss how researchers need to consider the impact of Reddit’s algorithms, affordances, and generalizability of the scientific knowledge produced using Reddit data, as well as the potential ethical dimensions of research that draws data from subreddits with potentially sensitive populations. 

The growing prevalence of data-rich networked information technologies—such as social media platforms, smartphones, wearable devices, and the internet of things —brings an increase in the flow of rich, deep, and often identifiable personal information available for researchers. More than just “big data,” these datasets reflect people’s lives and activities, bridge multiple dimensions of a person’s life, and are often collected, aggregated, exchanged, and mined without them knowing. We call this data “pervasive data,” and the increased scale, scope, speed, and depth of pervasive data available to researchers require that we confront the ethical frameworks that guide such research activities. Multiple stakeholders are embroiled in the challenges of research ethics in pervasive data research: researchers struggle with questions of privacy and consent, user communities may not even be aware of the widespread harvesting of their data for scientific study, platforms are increasingly restricting researcher’s access to data over fears of privacy and security, and ethical review boards face increasing difficulties in properly considering the complexities of research protocols relying on user data collected online. The results presented in this paper expand our understanding of how ethical review board members think about pervasive data research. It provides insights into how IRB professionals make decisions about the use of pervasive data in cases not obviously covered by traditional research ethics guidelines, and points to challenges for IRBs when reviewing research protocols relying on pervasive data. 

Around the world, people increasingly generate data through their everyday activities. Much of this also happens unwittingly, thanks to sensors, cameras, and other surveillance tools on the roads, in cities, and in businesses. However, the ways citizens and governments think about privacy vary significantly around the world. In this paper, we explore differences between citizens’ attitudes toward privacy and data collection practices in the U.S. and the Netherlands, an EU member nation. Using a factorial vignette survey methodology, we identify specific contextual factors associated with people’s level of concern about how their data is being used. We consider the role that five factors play in this assessment: actors (those using data), data type and amount, reported purpose for data use, and inferences drawn from the data. These indicate nationally bound differences but likewise point to potentially more globally shared concerns. 

Smart home technologies on our phones and in our homes are collecting more (quantity) and more (types of) data about us. These tools are intended to simplify everyday tasks, but to do so effectively, they collect significant data about users and their environment. One popular example includes intelligent personal assistants (IPAs) like Apple’s Siri, Amazon’s Alexa, and Google’s Assistant. As these services have transitioned from being embedded in consumers’ smartphones to standalone devices in the home, they have evolved to collect and share more data in more potentially invasive ways. In this paper, we consider American adults’ attitudes toward IPAs. Through an analysis of focus groups with IPA users and non-users, we describe attitudes and concerns toward IPAs broadly, as well as how these attitudes vary based on the devices’ features. We suggest new frameworks for evaluating privacy-based decisions and offer policy suggestions for regulating data flows from smart technologies. 

Background Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. Objective This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results While nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines.